1 Purpose

This document summarizes the results of new additions and edits to the initial analyses presented here. Notably, our analysis now includes Pink and Chum salmon stocks (N = 67, N = 48) from across the North Pacific Ocean, as well as newly added Sockeye stocks (N = 77).

2 Methods

We aim to characterize relationships between ocean conditions (SST and abundance of potential competitors) and salmon productivity (R/S) over space and time. There have been minor changes to our modelling approach since our last update.

2.1 Data

We have compiled spawner-recruitment time series for all five species of Pacific salmon - available here. The analyses presented here include 192 of these populations of Sockeye, Pink, and Chum salmon originating from Washington to the Bering Sea.

2.1.1 Map

Clicking on the legend items on the right hand side of the plot below will allow you to toggle between species.

Figure 1. Ocean entry locations of Sockeye (n=77), Pink (n=67), and Chum (n=48) salmon stocks included in analyses (total N=192).

2.1.2 Time Series Length

Productivity (log[R/S]) time series of all stocks are shown below to illustrate the length and relative number of time series among regions and species. Vertical dashed lines represent breakpoints in ‘Era’ models and illustrate the coverage of data over periods of interest.

2.1.2.1 Sockeye

Figure 2. Productivity (log[R/S]) time series of Sockeye stocks (n=77) with vertical dashed lines indicating proposed ocean regime shifts.

2.1.2.2 Pink

  • add proposed regime shifts to plots below

Figure 3. Productivity (log[R/S]) time series of Pink stocks (n=67) with vertical dashed lines indicating proposed ocean regime shifts.

2.1.2.3 Chum

Figure 4. Productivity (log[R/S]) time series of Chum stocks (n=48) with vertical dashed lines indicating proposed ocean regime shifts.

2.2 Models

We use 3(4) classes of generalized spawner-recruitment models:

  1. Stationary models (e.g. Connors et al. 2020), which estimate time-invariant relationships. These have not changed other than the addition of new data.

  2. ‘Era’ models (e.g., Malick 2020), which allow relationships to vary among pre-defined periods that represent hypothesized shifts in NP Ocean processes and relationships. The 1976/77 ‘regime shift’ is no longer modelled following feedback from our last update, which brought to light that this shift from a cold to warm PDO phase is not a true ‘regime shift’. New iterations of these models consider a second potential regime shift at the onset of the ~2013 marine heatwave (‘the blob’). Currently, we consider brood years >= 2011 to have interacted with the marine heatwave regardless of the species and stock. In future analyses, the timing can be altered to better reflect the diverse life histories of populations in the analysis.

  3. Random walk models (e.g., Malick 2020), which allow relationships to evolve gradually through time. These have not changed other than the addition of new data, however, a version that estimates ocean basin-scale trends in relationships is currently in development.

  4. Hidden Markov models, which allow relationships to vary according to latent states, are not included in this update. They may be revisited in the future.

Details of each model class and our Bayesian model fitting procedure can be found here.

2.3 Covariate analyses

We continue to focus on:

  1. Sea surface temperature (SST) at juvenile salmon ocean entry points, indexing local ocean conditions early in marine life when temperature is hypothesized to be most influential to marine survival (Mueter et al. 2002).

The resolution of SST data is currently 2 x 2 degrees (ERSST). SST at ocean entry points are averaged spatially across 400 square km, and temporally across a 3-month period immediately following ocean entry for most stocks, to get a simple index of SST. Future analyses of SST may include:

  • Sensitivity analysis of spatial averaging of SST over smaller (200 sq. km) or broader (800 sq. km) areas, to test alternative hypotheses about the distance over which initial ocean temperatures affect juvenile salmon as they migrate;
  • Sensitivity analysis of temporal SST averaging windows, including spring, summer, and winter of the first year at sea, to test alternative hypotheses about the time at which juvenile salmon are most affected by temperature;
  • Inclusion of high-resolution SST data. However, these will remain consistent with the general hypothesis that juvenile salmon are impacted by temperature in the first year of marine life, and finer-scale analyses of SST conditions remain outside the scope of this project.
  1. The abundance of salmon in the North Pacific potentially competing directly or indirectly for food.

( Brendan to fill in more about competitor analyses)

3 Results

3.1 Stationary models

3.1.1 Sockeye

Figure 5. Posterior probability distributions of the predicted effect of SST (top), competitor abundance (middle), and the combined effect (bottom) on Sockeye productivity (R/S). Faint lines show stock-specific effects while bold lines show regional effects from hierarchical model. X-axis values represent the percent change in productivity per standard deviation unit increase in the covariate.

3.1.2 Pink

Figure 6. Posterior probability distributions of the predicted effect of SST (top), competitor abundance (middle), and the combined effect (bottom) on Pink salmon productivity (R/S). Faint lines show stock-specific effects while bold lines show regional effects from hierarchical model. X-axis values represent the percent change in productivity per standard deviation unit increase in the covariate.

3.1.3 Chum

Figure 7. Posterior probability distributions of the predicted effect of SST (top), competitor abundance (middle), and the combined effect (bottom) on Chum salmon productivity (R/S). Faint lines show stock-specific effects while bold lines show regional effects from hierarchical model. X-axis values represent the percent change in productivity per standard deviation unit increase in the covariate.

3.2 Era models

3.2.1 Sockeye

Figure 8. Posterior probability distributions of the predicted effect of SST and competitors on Sockeye productivity over three pre-defined time periods/eras (earliest in top panel). Regional mean effects are shown by bold lines and individual stocks’ distributions by light lines.

3.2.2 Pink

Figure 9. Posterior probability distributions of the predicted effect of SST and competitors on Pink salmon productivity over three pre-defined time periods/eras (earliest in top panel). Regional mean effects are shown by bold lines and individual stocks’ distributions by light lines. Note that data from Alaska for the most recent time period are needed (Figure 3).

3.2.3 Chum

Figure 10. Posterior probability distributions of the predicted effect of SST and competitors on Chum salmon productivity over three pre-defined time periods/eras (earliest in top panel). Regional mean effects are shown by bold lines and individual stocks’ distributions by light lines. Note that data from Alaska for the most recent time period are needed, and data in the Southeast Alaska region are sparse across all time periods (Figure 4).

3.3 Random Walk models

3.3.1 Sockeye

Figure 11. Time-varying posterior mean estimates of SST and Competitor covariate effects on Sockeye productivity, modelled as a random walk. Individual stock estimates are in faint lines, while regional means and 80% CI are represented by bold lines and shaded areas. Region-wide means and CI are post-hoc calculations, rather than resulting from hierarchical model structures as in the stationary and era models.

3.3.2 Pink

Figure 12. Time-varying posterior mean estimates of SST and Competitor covariate effects on Pink salmon productivity, modelled as a random walk. Individual stock estimates are in faint lines, while regional means and 80% CI are represented by bold lines and shaded areas. Solid bold lines represent odd-year Pink stocks, while dashed lines are even-year stocks. Region-wide means and CI are post-hoc calculations, rather than resulting from hierarchical model structures as in the stationary and era models.

3.3.3 Chum

Figure 13. Time-varying posterior mean estimates of SST and Competitor covariate effects on Chum salmon productivity, modelled as a random walk. Individual stock estimates are in faint lines, while regional means and 80% CI are represented by bold lines and shaded areas. Region-wide means and CI are post-hoc calculations, rather than resulting from hierarchical model structures as in the stationary and era models.

4 Inferences and Next steps

Not finished, could use feedback! **********

Inference - We see evidence of nonstationarity across all 3 species - The degree of change varies among regions and species - There is some evidence that the ‘marine heatwave’ period changed ocean condition-productivity relationships

Next steps - Obtain updated data for Alaskan Pink and Chum stocks - Carry out sensitivity analyses for covariates as described - Make era model breaks consistent with each species’ life history - Continue work on modelling regional effects as a random walk